Search Results for author:
Found 19 papers, 8 papers with code
SplatTalk: 3D VQA with Gaussian Splatting
Language-guided 3D scene understanding is important for advancing applications in robotics, AR/VR, and human-computer interaction, enabling models to comprehend and interact with 3D environments through natural language.
Gaussian3Diff: 3D Gaussian Diffusion for 3D Full Head Synthesis and Editing
We present a novel framework for generating photorealistic 3D human head and subsequently manipulating and reposing them with remarkable flexibility.
NIFTY: Neural Object Interaction Fields for Guided Human Motion Synthesis
This interaction field guides the sampling of an object-conditioned human motion diffusion model, so as to encourage plausible contacts and affordance semantics.
Nerflets: Local Radiance Fields for Efficient Structure-Aware 3D Scene Representation from 2D Supervision
We address efficient and structure-aware 3D scene representation from images.
Polynomial Neural Fields for Subband Decomposition and Manipulation
We use this framework to design Fourier PNFs, which match state-of-the-art performance in signal representation tasks that use neural fields.
OpenScene: 3D Scene Understanding with Open Vocabularies
Traditional 3D scene understanding approaches rely on labeled 3D datasets to train a model for a single task with supervision.
Ranked #7 on
3D Open-Vocabulary Instance Segmentation
on Replica
3D Open-Vocabulary Instance Segmentation
3D Semantic Segmentation
+1
Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation
Our model builds a panoptic radiance field representation of any scene from just color images.
NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes
We present NeSF, a method for producing 3D semantic fields from posed RGB images alone.
Learning 3D Semantic Segmentation with only 2D Image Supervision
With the recent growth of urban mapping and autonomous driving efforts, there has been an explosion of raw 3D data collected from terrestrial platforms with lidar scanners and color cameras.
Ranked #8 on
LIDAR Semantic Segmentation
on nuScenes
Multiresolution Deep Implicit Functions for 3D Shape Representation
To the best of our knowledge, MDIF is the first deep implicit function model that can at the same time (1) represent different levels of detail and allow progressive decoding; (2) support both encoder-decoder inference and decoder-only latent optimization, and fulfill multiple applications; (3) perform detailed decoder-only shape completion.
Differentiable Surface Rendering via Non-Differentiable Sampling
We present a method for differentiable rendering of 3D surfaces that supports both explicit and implicit representations, provides derivatives at occlusion boundaries, and is fast and simple to implement.
IBRNet: Learning Multi-View Image-Based Rendering
Unlike neural scene representation work that optimizes per-scene functions for rendering, we learn a generic view interpolation function that generalizes to novel scenes.
Local Deep Implicit Functions for 3D Shape
The goal of this project is to learn a 3D shape representation that enables accurate surface reconstruction, compact storage, efficient computation, consistency for similar shapes, generalization across diverse shape categories, and inference from depth camera observations.
Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation
We design a simple but surprisingly effective visual recognition benchmark for studying bias mitigation.
Ranked #1 on
Out-of-Distribution Generalization
on UrbanCars
CvxNet: Learnable Convex Decomposition
We introduce a network architecture to represent a low dimensional family of convexes.
Text-based Editing of Talking-head Video
To edit a video, the user has to only edit the transcript, and an optimization strategy then chooses segments of the input corpus as base material.
Learning Shape Templates with Structured Implicit Functions
To allow for widely varying geometry and topology, we choose an implicit surface representation based on composition of local shape elements.
Unsupervised Training for 3D Morphable Model Regression
We train a regression network using these objectives, a set of unlabeled photographs, and the morphable model itself, and demonstrate state-of-the-art results.
Ranked #2 on
3D Face Reconstruction
on Florence
(Average 3D Error metric)
Learning Where to Look: Data-Driven Viewpoint Set Selection for 3D Scenes
We provide a search algorithm that generates a sampling of likely candidate views according to the example distribution, and a set selection algorithm that chooses a subset of the candidates that jointly cover the example distribution.
